在贝叶斯统计中，某一不确定量p的先验概率分布是在考虑"观测数据"前，能表达p不确定性的概率分布。它旨在描述这个不确定量的不确定程度，而不是这个不确定量的随机性。这个不确定量可以是一个参数，或者是一个隐含变量（英语：latent variable）。

在使用贝叶斯定理时，我们通过将先验概率与似然函数相乘，随后标准化，来得到后验概率分布，也就是给出某数据，该不确定量的条件分布。

先验概率通常是主观的猜测，为了使计算后验概率方便，有时候会选择共轭先验。如果后验概率和先验概率是同一族的，则认为它们是共轭分布，这个先验概率就是对应于似然函数的共轭先验。

In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. For example, the prior could be the probability distribution representing the relative proportions of voters who will vote for a particular politician in a future election. The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable.